xsum

Warning: Manual download required. See instructions below.

• Description:

Extreme Summarization (XSum) Dataset.

There are two features: - document: Input news article. - summary: One sentence summary of the article.

This data need to manaully downloaded and extracted as described in https://github.com/EdinburghNLP/XSum/blob/master/XSum-Dataset/README.md The folder 'xsum-extracts-from-downloads' need to be compressed as 'xsum-extracts-from-downloads.tar.gz' and put in manually downloaded folder.

• Additional Documentation: Explore on Papers With Code

• Homepage: https://github.com/EdinburghNLP/XSum/tree/master/XSum-Dataset

• Source code: tfds.summarization.Xsum

• Versions:

  • 1.0.0: Dataset without cleaning.
  • 1.1.0 (default): Removes web contents.

• Download size: 2.59 MiB

• Dataset size: 512.03 MiB

• Manual download instructions: This dataset requires you to download the source data manually into download_config.manual_dir (defaults to ~/tensorflow_datasets/downloads/manual/):
  Detailed download instructions (which require running a custom script) are here: https://github.com/EdinburghNLP/XSum/blob/master/XSum-Dataset/README.md#running-the-download-and-extraction-scripts Afterwards, please put xsum-extracts-from-downloads.tar.gz file in the manual_dir.

• Auto-cached (documentation): No

• Splits:

Split	Examples
'test'	11,301
'train'	203,577
'validation'	11,305

• Feature structure:

FeaturesDict({
    'document': Text(shape=(), dtype=string),
    'summary': Text(shape=(), dtype=string),
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
document	Text		string
summary	Text		string

• Supervised keys (See as_supervised doc): ('document', 'summary')

• Figure (tfds.show_examples): Not supported.

• Examples (tfds.as_dataframe):

• Citation:

@article{Narayan2018DontGM,
  title={Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization},
  author={Shashi Narayan and Shay B. Cohen and Mirella Lapata},
  journal={ArXiv},
  year={2018},
  volume={abs/1808.08745}
}